Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding

Abstract

Background: Besides their other roles, brain imaging and other biomarkers of Alzheimer’s disease (AD) have the potential to inform a cognitively unimpaired (CU) person’s likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures. Objective: To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). Methods: Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years (“progressors”) and 80 matched adults who remained CU for at least 4 years (“nonprogressors”). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. Results: We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume. Conclusion: Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

“…The second contribution is that the FMFS is an effective tool to select and visualize the brain imaging feature data. In our previous studies (Stonnington et al, 2021;Zhang et al, 2021a,b), the morphometry features always showed excellent performance in predicting AD progression. However, the major limitation of these works was that they failed to visualize the disease-related regions on the surfaces.…”

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

“…The second contribution is that the FMFS is an effective tool to select and visualize the brain imaging feature data. In our previous studies (Stonnington et al, 2021;Zhang et al, 2021aZhang et al, , 2021b, the morphometry features always showed excellent performance in predicting AD progression. However, the major limitation of these works was that they failed to visualize the disease-related regions on the surfaces.…”

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons With and Without Significant Amyloid Plaques

“…[30][31][32]We also performed surface smoothing to reduce the noise, followed by mesh simplification and refinement using the same method as our previous work [40–42]. A conformal grid of size 150*100 was computed on each surface using a holomorphic 1-form basis [43,44], followed by the computation of conformal factor and mean curvature on each point and a surface fluid registration method to register the hippocampal surfaces to a template surface [45–47][40] For more details, refer to [45].…”

“…We took advantage of our previously established pipeline protocol for hippocampus segmentation and image registration [14,28,29], which employs FMRIB's Integrated Registration and Segmentation Tool (FIRST) for automatic segmentation, a topology-preserving level set method combined with marching cubes algorithm for hippocampal surface reconstruction [37][38][39]. [30][31] [32]We also performed surface smoothing to reduce the noise, followed by mesh simplification and refinement using the same method as our previous work [40][41][42]. A conformal grid of size 150*100 was computed on each surface using a holomorphic 1-form basis [43,44], followed by the computation of conformal factor and mean curvature on each point and a surface fluid registration method to register the hippocampal surfaces to a template surface [45][46][47][40] For more details, refer to [45].…”